Affiliation:
1. Verus Research, Albuquerque, New Mexico 87110
2. Air Force Research Laboratory, Space Vehicles Directorate, Kirtland Air Force Base, New Mexico 87123
Abstract
As the number of on-orbit satellites increases, the ability to repair or de-orbit them is becoming increasingly important. The implicitly required task of on-orbit inspection is challenging due to coordination of multiple observer satellites, a highly nonlinear environment, a potentially unknown or unpredictable target, and time delays associated with ground-based control. There is a critical need for autonomous, robust, decentralized solutions. To achieve this, we consider a hierarchical, learned approach for the decentralized planning of multi-agent inspection of a tumbling target. Our solution consists of two components: a viewpoint or high-level planner trained using deep reinforcement learning, and a low-level planner that will handle the point-to-point maneuvering of the spacecraft. Operating under limited information, our trained multi-agent high-level policies successfully contextualize information within the global hierarchical environment and are correspondingly able to inspect over 90% of nonconvex tumbling targets, even in the absence of additional agent attitude control.
Funder
Air Force Research Laboratory
Publisher
American Institute of Aeronautics and Astronautics (AIAA)
Subject
Space and Planetary Science,Aerospace Engineering